A retinal blood vessel segmentation based on improved D-MNet and pulse-coupled neural network

Deng, Xiangyu; Ye, Jinhong

doi:10.1016/j.bspc.2021.103467

Cited by 39 publications

(14 citation statements)

References 28 publications

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“…We further compare the performance of BCR-Net with multiple state-of-the-art and widely used methods. As shown in Tables 5 , 6 , M-Net ( 35 ), AG-Net ( 16 ), RSAN ( 36 ), NFN+( 37 ), Pyramid U-Net ( 21 ), SCS-Net ( 38 ), Deng and Ye ( 39 ) and Xu et al ( 40 ) gave the experimental results of DRIVE and CHASE DB1 in the original paper, and also gave STARE and IOSTAR in part. For the other five methods, including U-Net ( 5 ), Attention UNet ( 41 ), SD-UNet ( 24 ), MultiResUNet ( 27 ) and DRNet ( 20 ), we conduct experiments on four datasets (DRIVE, CHASEDB1, STARE, and IOSTAR) based on the same training strategy and parameter settings as BCR-UNet.…”

Section: Experiments and Resultsmentioning

confidence: 99%

BCR-UNet: Bi-directional ConvLSTM residual U-Net for retinal blood vessel segmentation

Guo

et al. 2022

Front. Public Health

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BackgroundHigh precision segmentation of retinal blood vessels from retinal images is a significant step for doctors to diagnose many diseases such as glaucoma and cardiovascular diseases. However, at the peripheral region of vessels, previous U-Net-based segmentation methods failed to significantly preserve the low-contrast tiny vessels.MethodsFor solving this challenge, we propose a novel network model called Bi-directional ConvLSTM Residual U-Net (BCR-UNet), which takes full advantage of U-Net, Dropblock, Residual convolution and Bi-directional ConvLSTM (BConvLSTM). In this proposed BCR-UNet model, we propose a novel Structured Dropout Residual Block (SDRB) instead of using the original U-Net convolutional block, to construct our network skeleton for improving the robustness of the network. Furthermore, to improve the discriminative ability of the network and preserve more original semantic information of tiny vessels, we adopt BConvLSTM to integrate the feature maps captured from the first residual block and the last up-convolutional layer in a nonlinear manner.Results and discussionWe conduct experiments on four public retinal blood vessel datasets, and the results show that the proposed BCR-UNet can preserve more tiny blood vessels at the low-contrast peripheral regions, even outperforming previous state-of-the-art methods.

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Section: Experiments and Resultsmentioning

confidence: 99%

BCR-UNet: Bi-directional ConvLSTM residual U-Net for retinal blood vessel segmentation

Guo

et al. 2022

Front. Public Health

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“…Up to now, the model has been widely used in various fields of digital image processing and has achieved good results. PCNN is a mathematical model that simulates the relationship between the structure of biological neurons and the interaction between neurons [ 15 ], and its simplified model is shown in Figure 1 .…”

Section: Theoretical Analysis Of Chaotic Pcnn Modelmentioning

confidence: 99%

A Novel Image Encryption Algorithm Based on Improved Arnold Transform and Chaotic Pulse-Coupled Neural Network

Deng

Zhang

et al. 2022

Entropy

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Information security has become a focal topic in the information and digital age. How to realize secure transmission and the secure storage of image data is a major research focus of information security. Aiming at this hot topic, in order to improve the security of image data transmission, this paper proposes an image encryption algorithm based on improved Arnold transform and a chaotic pulse-coupled neural network. Firstly, the oscillatory reset voltage is introduced into the uncoupled impulse neural network, which makes the uncoupled impulse neural network exhibit chaotic characteristics. The chaotic sequence is generated by multiple iterations of the chaotic pulse-coupled neural network, and then the image is pre-encrypted by XOR operation with the generated chaotic sequence. Secondly, using the improved Arnold transform, the pre-encrypted image is scrambled to further improve the scrambling degree and encryption effect of the pre-encrypted image so as to obtain the final ciphertext image. Finally, the security analysis and experimental simulation of the encrypted image are carried out. The results of quantitative evaluation show that the proposed algorithm has a better encryption effect than the partial encryption algorithm. The algorithm is highly sensitive to keys and plaintexts, has a large key space, and can effectively resist differential attacks and attacks such as noise and clipping.

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“… 23 Deng and Ye (2022) performed retinal blood vessel segmentation based on an improved deformable convolutional M-shaped network and a pulse-coupled neural network. 24 Zhang et al (2022) presented a novel automatic method based on bridge-net by joint learning context-involved and non-context features for the segmentation of retinal blood vessels. 25 In the regression task, researchers have attempted to develop intelligence-based approaches to reliably predict blood pressure.…”

Section: Introductionmentioning

confidence: 99%

“…Davamani et al (2022) developed fuzzy c-means clustering for blood cell classification . In addition, others have developed AI-based models to segment blood vessels. − Wang et al (2015) integrated CNN and random forest (RF) for retinal blood vessel segmentation . Soomro et al.…”

Section: Introductionmentioning

confidence: 99%

“…Gegundez-Arias et al (2021) developed a new deep learning method for blood vessel segmentation in retinal images based on convolutional kernels and a modified U-net model . Deng and Ye (2022) performed retinal blood vessel segmentation based on an improved deformable convolutional M-shaped network and a pulse-coupled neural network . Zhang et al (2022) presented a novel automatic method based on bridge-net by joint learning context-involved and non-context features for the segmentation of retinal blood vessels .…”

Section: Introductionmentioning

confidence: 99%

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Refractive Index of Hemoglobin Analysis: A Comparison of Alternating Conditional Expectations and Computational Intelligence Models

Alizamir

Gholami

Bahrami³

et al. 2022

ACS Omega

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Hemoglobin is one of the most important blood elements, and its optical properties will determine all other optical properties of human blood. Since the refractive index (RI) of hemoglobin plays a vital role as a non-invasive indicator of some illnesses, accurate calculation of it would be of great importance. Moreover, measurement of the RI of hemoglobin in the laboratory is time-consuming and expensive; thus, developing a smart approach to estimate this parameter is necessary. In this research, four viable strategies were used to make a quantitative correlation between the RI of hemoglobin and its influencing parameters including the concentration, wavelength, and temperature. First, alternating conditional expectations (ACE), a statistical approach, was employed to generate a correlation to predict the RI of hemoglobin. Then, three different optimized intelligent techniques—optimized neural network (ONN), optimized fuzzy inference system (OFIS), and optimized support vector regression (OSVR)—were used to model the RI. A bat-inspired (BA) algorithm was embedded in the formulation of intelligent models to obtain the optimal values of weights and biases of an artificial neural network, membership functions of the fuzzy inference system, and free parameters of support vector regression. The coefficient of determination, root-mean-square error, average absolute relative error, and symmetric mean absolute percentage error for each of the ACE, ONN, OFIS, and OSVR were found as the measure of each model’s accuracy. Results showed that ACE and optimized models (ONN, OFIS, and OSVR) have promising results in the estimation of hemoglobin’s RI. Collectively, ACE outperformed ONN, OFIS, and OSVR, while sensitivity analysis indicated that the concentration, wavelength, and, lastly, temperature would have the highest impact on the RI.

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A retinal blood vessel segmentation based on improved D-MNet and pulse-coupled neural network

Cited by 39 publications

References 28 publications

BCR-UNet: Bi-directional ConvLSTM residual U-Net for retinal blood vessel segmentation

BCR-UNet: Bi-directional ConvLSTM residual U-Net for retinal blood vessel segmentation

A Novel Image Encryption Algorithm Based on Improved Arnold Transform and Chaotic Pulse-Coupled Neural Network

Refractive Index of Hemoglobin Analysis: A Comparison of Alternating Conditional Expectations and Computational Intelligence Models

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